14 citations · 18 across the 3 of their papers we have counts for
3 papers
cs.IR2024★ 3 cited
Large Language Models as Conversational Movie Recommenders: A User Study
Ruixuan Sun, Xinyi Li, Avinash Akella +1
This paper explores the effectiveness of using large language models (LLMs) for personalized movie recommendations from users' perspectives in an online field experiment. Our study…
cs.IR2024★ 1 cited
What Are We Optimizing For? A Human-centric Evaluation of Deep Learning-based Movie Recommenders
Ruixuan Sun, Xinyi Wu, Avinash Akella +3
In the past decade, deep learning (DL) models have gained prominence for their exceptional accuracy on benchmark datasets in recommender systems (RecSys). However, their evaluation…
cs.HC2023★ 14 cited
Interactive Content Diversity and User Exploration in Online Movie Recommenders: A Field Experiment
Ruixuan Sun, Avinash Akella, Ruoyan Kong +2
Recommender systems often struggle to strike a balance between matching users' tastes and providing unexpected recommendations. When recommendations are too narrow and fail to cove…